Multi-scale land-use disaggregation modelling: Concept and application to EU countries

Changes of carbon stocks in agricultural soils, emissions of greenhouse gases from agriculture, and the delivery of ecosystem services of agricultural landscapes depend on combinations of land-use, livestock density, farming practices, climate and soil types. Many environmental processes are highly non-linear. If the analysis of the environmental impact is based on data at a relatively coarse-scale (e.g. farm, country, or large administrative regions), conclusions can be misleading. For an accurate assessment of agri-environmental indicators, data of agricultural activities and their dynamics are needed at high spatial resolution. In this paper, we develop and validate a spatial model for predicting the agricultural land-use areas within the homogenous spatial units (HSUs). For the EU-28 countries, we distinguish about 1.5 × 10⁵ HSUs and we consider 30 possible land-uses to match with the classification used in the Common Agricultural Policy Regionalized Impact (CAPRI) model. The comparison of model predictions with independent observations and with a simple rule-based approach at HSU level demonstrates that the predictions are generally accurate in more than 75 % of HSUs. The frequent crops or land-use are better predicted. For non-frequent crops and/or crops requiring specific cultivation conditions, the model needs further fine-tuning.     

堆肥过程中基本条件的控制

概述了堆肥过程中各种基本条件控制因子,包括含水量、通风供氧、温度、挥发性有机物质、C/N比和C/P比、颗粒大小和容积密度、pH值,对其影响作用和如何调控进行综述,为堆肥的顺利进行提供理论基础和实践参考。     

How a Pareto frontier complements scenario projections in land use change impact assessment

To evaluate the sustainability of potential agricultural land developments, scenario projections with land use change models are often combined with environmental impact assessments. Although this allows inter-scenario comparison of impacts, it does not permit interpretation of scenarios in the light of theoretically optimal impacts. A Pareto frontier provides this information. We demonstrate this for ethanol production in Goiás, Brazil, in 2030. For a Business-as-Usual scenario projection, the spatial configuration, production costs, and GHG emissions of the production chain are compared with those obtained from spatial optimization and summarized by the Pareto frontier. Projected production costs are 729 $/m³ ethanol, with GHG emissions of 40 kg CO₂-eq/m³ ethanol. The Pareto frontier indicates an improvement potential of ∼50 $/m³ ethanol when keeping emissions fixed, or ∼250 kg CO₂-eq/m³ ethanol when keeping costs fixed. Robust locations having low costs and emissions show where and how improvements are reached, offering instruments for policy (re)design.     

Stormwater runoff plumes observed by SeaWiFS radiometer in the Southern California Bight

Understanding the factors that influence the incidence and dispersal patterns of freshwater runoff plumes in southern California is important for management of coastal water quality. Significant river discharge is associated with episodic winter rainstorms, leading to turbid pollutant and pathogen-laden stormwater plumes that are clearly visible nearshore in the Southern California Bight. We analyzed 1.1-km spatial resolution sea-spectral reflectance data acquired in 1997-2003 by the Sea-viewing Wide Field-of-view Sensor (SeaWiFS), focusing on four regions with distinctive adjacent watershed properties: Ventura, Santa Monica Bay, San Pedro Shelf, and Orange County/San Diego. The area of each plume was detected by the backscattering characteristics of surface waters, i.e., normalized water-leaving radiation of green-yellow wavelength 555 nm (nLw555). Plume area size was correlated with the rainstorm magnitude, which was estimated from atmospheric precipitation averaged over the total area of the watersheds connected to the seashore. The time lag between rainstorm and maximum plume was one day in San Pedro Shelf and two days in other three regions. Assessing maximum correlation between precipitated rainwater and the plume size, we estimated the optimal nLw555 values best characterizing the plume boundaries in each of the four study regions. Another quantitative characteristic derived from maximum correlation between rainwater and plume size was the “coefficient of persistence”, related to the speed of freshwater discharge and the time of plume water dissipation; it was also different in different watersheds. The primary factors regulating the relationship between rainstorm and plume were watershed land-use characteristics, size, and elevation.     

Spatial aspects affecting acidification factors in European acidification modelling

Plain linear models have recently been used in methodologies to model fate and transport for assessing acidification in life cycle impact assessment (LCIA), or in support of air pollution abatement policies. These models originate from a statistical analysis of the relationship between inputs and outputs of physically-based models that reflect the mechanics of a system in detail. Linear models applied to assess acidification use an acidification factor (AF), which relates changes in the magnitude of emissions to changes in the total area that is protected against acidification in Europe. The changes in emission volume refer to changes of one substance, within one country and one sector or one grid cell.This paper evaluates the dependence of AFs on three spatial characteristics, i.e. the spatial emission and deposition resolution, the spatial emission distribution and the actual spatial location of emissions.The applied spatial resolutions of emission and deposition cause non-systematic variations in AFs of up to 60%, relative to the finest resolution. The manner in which the distribution of emissions is modelled, i.e. grid or sector-specific, is shown to affect AFs considerably, as well. We conclude that spatial characteristics of the physically-based acidification model can affect the assessment of acidification by means of plain linear models.     

Agricultural land-use change in Brazilian Amazônia between 1980 and 1995: Evidence from integrated satellite and census data

As cropland and pasture have replaced forest and cerrado in Brazilian Amazônia, concern has mounted over the effects of changing the biogeochemical and hydrological properties of one of the world's great storehouses of biomass and biodiversity. Although much recent effort has focused on the location, effects, and causes of deforestation and cerrado conversion, much less is known about the basin-wide spatial distribution and density of the land use following conversion for crops or pasture.In this paper, we use census and satellite records to develop maps of the distribution and abundance of major agricultural land uses across 4.5×10⁸ ha of Brazilian Amazônia in 1980 and 1995. Results indicate an overall expansion of 7.0×10⁶ ha in total agricultural area in Brazilian Amazônia between 1980 and 1995. The net change during this period is estimated for three different land-use types: croplands (an increase of 0.8×10⁶ ha), natural pastures (a decrease of 8.4×10⁶ ha), and planted pastures (an increase of 14.7×10⁶ ha). These estimates, the first spatially explicit quantifications of agricultural land-use activities in 1980 and 1995 across Brazilian Amazônia, are shown to be consistent with the results of applying a land use change and secondary regrowth model to published deforestation rates for the period.The resulting time slices, presented for each land-use category at 5-min (∼9 km) spatial resolution, allow for the quantification of land-use changes in this region for biogeochemical, demographic and economic models. Several foci of agricultural change existed within Brazilian Amazônia during this period: in the state of Pará, cropland was lost and planted pasture increased markedly; in Mato Grosso, both cropland and planted pasture increased; in Rondônia, planted pasture replacing forest was the primary route to agricultural expansion.     

Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery

This paper presents the methodology used to detect temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta (VMD) based on MODIS time-series imagery (Wavelet-based Filter for detecting spatio-temporal changes in Flood Inundation; WFFI). This methodology involves the use of a wavelet-based filter to interpolate missing information and reduce the noise component in the time-series data, as proposed in a previous study. The smoothed time profiles of Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), and the Difference Value between EVI and LSWI (DVEL) are obtained from MOD09 8-day composite time-series data (resolution: 500 m; time period: 2000-2005). The proposed algorithm was applied to produce time-series inundation maps (WFFI products) for the five annual flood seasons over the period from 2000 to 2004. The WFFI products were validated via comparisons with Landsat-derived results and inundation maps based on RADARSAT images, hydrological data, and digital elevation model data. Compared with the RADARSAT-derived inundation maps at the province level, the obtained RMSE range from 364 to 443 km² and the determination coefficients [R²] range from 0.89 to 0.92. Compared with Landsat-derived results at the 10-km grid level, the obtained RMSE range from 6.8 to 15.2 km² and the determination coefficients [R²] range from 0.77 to 0.97. The inundated area of flooded forests/marsh to the northeast of Tonle Sap Lake were underestimated, probably because of extensive vegetation cover in this area. The spatial characteristics of the estimated start dates, end dates, and duration of inundation cycles were also determined for the period from 2000 to 2004. There are clear contrasts in the distribution of the estimated end dates and duration of inundation cycles between large-scale floods (2000-2002) and medium- and small-scale floods (2003 and 2004). At the regional scale, the estimated start dates for the southern part of An Giang Province during 2003 and 2004 was distinctly later than that for surrounding areas. The results indicate that these triple-cropping areas enclosed by dikes increased in extent from 2003 to 2004. In contrast, the estimated end dates of inundation at the Co Do and Song Hau State Farms were clearly earlier than those for surrounding areas, although the estimated start dates were similar. Temporal changes in the inundation area of Flood pixels in the Dong Thap and Long An Provinces are in excellent agreement with daily water-level data recorded at Tan Chau Station. The estimated area of Long-term water body increased in size from 2000 to 2004, especially in coastal areas of the Ca Mau and Bac Lieu Provinces. Statistical data for Vietnam indicate that this trend may reflect the expansion of shrimp-farming areas. The WFFI products enable an understanding of seasonal and annual changes in the water distribution and environment of the Cambodia and the VMD from a global viewpoint.     

A customisable downscaling approach for local-scale meteorological and air pollution forecasting: Performance evaluation for a year of urban meteorological forecasts

In this paper we develop a customisable downscaling approach for local-scale air quality and meteorological forecasting applications, using The Air Pollution Model (TAPM) with the Conformal Cubic Atmospheric Model (CCAM). The CCAM–TAPM system allows users to define their own local pollution emissions, terrain and land-use data and then downscale the meteorological and air pollution forecast on PC hardware. To evaluate the meteorological component of the CCAM–TAPM system, we have produced 7-day forecasts dynamically downscaled to 3 km resolution, centred on Melbourne (Australia). We produce 365, 7-day forecasts with a forecast starting on each day during 2003, initialised with National Centre for Environmental Prediction (NCEP) 1° Global Forecasting System (GFS) analyses (i.e., no boundary conditions are needed). We estimate that CCAM–TAPM can simulate 10-m wind speeds and 2-m temperatures for up to two forecast days with errors within 10% of previous TAPM studies in which air pollution was successfully predicted.     

Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products

Global NDVI data are routinely derived from the AVHRR, SPOT-VGT, and MODIS/Terra earth observation records for a range of applications from terrestrial vegetation monitoring to climate change modeling. This has led to a substantial interest in the harmonization of multisensor records. Most evaluations of the internal consistency and continuity of global multisensor NDVI products have focused on time-series harmonization in the spectral domain, often neglecting the spatial domain. We fill this void by applying variogram modeling (a) to evaluate the differences in spatial variability between 8-km AVHRR, 1-km SPOT-VGT, and 1-km, 500-m, and 250-m MODIS NDVI products over eight EOS (Earth Observing System) validation sites, and (b) to characterize the decay of spatial variability as a function of pixel size (i.e. data regularization) for spatially aggregated Landsat ETM+ NDVI products and a real multisensor dataset. First, we demonstrate that the conjunctive analysis of two variogram properties - the sill and the mean length scale metric - provides a robust assessment of the differences in spatial variability between multiscale NDVI products that are due to spatial (nominal pixel size, point spread function, and view angle) and non-spatial (sensor calibration, cloud clearing, atmospheric corrections, and length of multi-day compositing period) factors. Next, we show that as the nominal pixel size increases, the decay of spatial information content follows a logarithmic relationship with stronger fit value for the spatially aggregated NDVI products (R² = 0.9321) than for the native-resolution AVHRR, SPOT-VGT, and MODIS NDVI products (R² = 0.5064). This relationship serves as a reference for evaluation of the differences in spatial variability and length scales in multiscale datasets at native or aggregated spatial resolutions. The outcomes of this study suggest that multisensor NDVI records cannot be integrated into a long-term data record without proper consideration of all factors affecting their spatial consistency. Hence, we propose an approach for selecting the spatial resolution, at which differences in spatial variability between NDVI products from multiple sensors are minimized. This approach provides practical guidance for the harmonization of long-term multisensor datasets.     

High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines

Many developing countries in Asia are experiencing rapid urban expansion in climate hazard prone areas. To support climate resilient urban planning efforts, here we present an approach for simulating future urban land-use changes and evaluating potential flood exposure at a high spatial resolution (30 m) and national scale. As a case study, we applied this model to the Philippines – a country frequently affected by extreme rainfall events. Urban land-use changes were simulated to the year 2050 using a trend-based logistic regression cellular automata model, considering three different scenarios of urban expansion (assuming low/medium/high population growth). Flood exposure assessment was then conducted by overlaying the land-use simulation results onto a global floodplain map. We found that approximately 6040–13,850 ha of urban land conversion is likely to be located in flood prone regions between 2019 and 2050 (depending on the scenario), affecting approximately 2.5–5.8 million additional urban residents. In locations with high rates of future urban development in flood prone areas (Mindanao Island, in particular), climate resilient land-use plans should be developed/enforced, and flood mitigation infrastructure protected (in the case of “nature-based” infrastructure) or constructed. The data selected for our land-use change modeling and flood exposure assessment were all openly and (near-)globally available, with the intention that our methodology can potentially be applied in other countries where rapid urban expansion is occurring. The 2050 urban land-use maps generated in this study are available for download at https://www.iges.or.jp/en/pub/ph-urban2050/en to allow for their use in future works.     

Regional evaporation estimates from flux tower and MODIS satellite data

Two models were evaluated for their ability to estimate land surface evaporation at 16-day intervals using MODIS remote sensing data and surface meteorology as inputs. The first was the aerodynamic resistance-surface energy balance model, and the second was the Penman-Monteith (P-M) equation, where the required surface conductance is estimated from remotely-sensed leaf area index. The models were tested using 3 years of evaporation and meteorological measurements from two contrasting Australian ecosystems, a cool temperate, evergreen Eucalyptus forest and a wet/dry, tropical savanna. The aerodynamic resistance-surface energy balance approach failed because small errors in the radiative surface temperature translate into large errors in sensible heat, and hence into estimates of evaporation. The P-M model adequately estimated the magnitude and seasonal variation in evaporation in both ecosystems (RMSE = 27 W m^− 2, R² = 0.74), demonstrating the validity of the proposed surface conductance algorithm. This, and the ability to constrain evaporation estimates via the energy balance, demonstrates the superiority of the P-M equation over the surface temperature-based model. There was no degradation in the performance of the P-M model when gridded meteorological data at coarser spatial (0.05°) and temporal (daily) resolution were substituted for locally-measured inputs.The P-M approach was used to generate a monthly evaporation climatology for Australia from 2001 to 2004 to demonstrate the potential of this approach for monitoring land surface evaporation and constructing monthly water budgets from 1-km to continental spatial scales.     

Spatio-temporal distribution of burned areas by ecoregions in Mexico and Central America

Fire activity in Mexico and Central America, and its associated emissions, has impacts across multiple scales. On the local-to-regional scale, fire activity impacts land use, productivity, and biodiversity. On the regional-to-global scale, fire activity impacts hydrological, biogeochemical, and atmospheric processes. A consistent, reliable, large-scale characterization of the spatial and temporal distribution of fire burned area is required to assess its environmental impacts and to support natural resources’ management. The spatial and temporal distributions of fire burned areas in ecoregions of Mexico and Central America are evaluated in this study for the period 2001–2014, using the satellite Moderate Resolution Imaging Spectroradiometer (MODIS) MCD45 Burned Area data set. The methodology combines the 500 m burned area product with a MODIS land cover product and a map of North American land cover to calculate the spatiotemporal variability of fire activity as a function of land-use type.

The total burned area over Mexico and Central America over the period 2001–2014 was found to be 614,243.5 km², but with significant interannual variability over the 14 years included in the study. Indeed, the minimum burned area over the period was 9892.25 km² in 2014 and the maximum was 37,669.50 km² in 2011, a fourfold increase. Burned areas were found to be concentrated in northern Mexico and on the Pacific coast, mainly from October to June. Agricultural burned areas accounted for 37% and 43% of total detected burns in Mexico and Central America, respectively. The largest extent of burned surface occurs in May for most land-cover types. The maximum density of burned areas occurred in the tropical dry forests ecoregion during the dry season. Both in Mexico and Central America, burned area anomalies have significant anti-correlation with precipitation anomalies.     

Modeling historical fecal coliform loadings to large European rivers and resulting in-stream concentrations

Information on fecal coliform (FC) concentrations in European rivers is scarce. The objective of this study was to identify hotspots of water pollution in Europe and provide information on the different FC sources and their contributions to the loads that lead to concentrations in rivers. Model simulations were carried-out with the large-scale water quality model WorldQual to assess the calculated loads regarding to its associated sources and to further estimate the related in-stream concentration. For the year 1995, model results indicated that FC loadings were higher in central Europe with 500 to above 2000 10¹⁰ cfu km⁻² a⁻¹ than in northern and eastern Europe where loadings ranged between 0 and 200 10¹⁰ cfu km⁻² a⁻¹. Major sources of FC loadings are domestic sewage, followed by scattered settlements (private treatment), urban surface runoff and manure application. Concentrations showed similar regional patterns as loadings, with high concentrations in central Europe and low concentrations in northern and eastern Europe.     

Inferring the source strength of isoprene from ambient concentrations

This paper reports on the application of an inverse Lagrangian technique that uses localized near-field (LNF) theory to calculate the source strength profile of isoprene from deciduous forest canopies. The basic tenet considered in this study is that the prevailing ambient isoprene concentrations observed over forests represent the source strength of the underlying surface as the scalar is transported from the sites of biosynthesis to the measurement point above forest canopies. Using information on the distribution of active isoprene biomass and the plant canopy environment, a two-storey canopy model was developed and applied to estimate isoprene emission rate profiles for a monoculture aspen forest whose isoprene source is homogeneously distributed throughout the landscape. Modelled results show that isoprene emission rates strongly vary with canopy depth, with maximum values coinciding with canopy layers with largest amount of active biomass. The model also captures the strong diurnal patterns of isoprene emissions from the forest canopy. We conclude that the present modelling system provides a practical method for estimating isoprene emission rate profiles based on the knowledge of atmospheric turbulence and ambient concentrations.     

Measurement of oak tree density with Landsat TM data for estimating biogenic isoprene emissions in Tennessee,USA

Isoprene emissions from oak trees in the eastern USA play an important role in tropospheric ozone pollution. Oak trees (Quercus) emit an order of magnitude more isoprene than most other emitting tree species, and are by far the largest source of biogenic isoprene in the eastern US. We used Landsat TM data to measure oak tree abundance near Oak Ridge, Tennessee, to estimate fluxes of isoprene. The Landsat classification was performed using multi-date data, supervised classification techniques, and an iterative approach. Training sites were selected based on transect data, and ten vegetation classes were mapped. A supervised classification algorithm called the Spectral Angle Mapper was used to classify the data. Empirical vegetation emission data were used to estimate the isoprene flux from each of the vegetation classes. The resultant isoprene flux maps were compared with concentrations measured in the field, and a good correspondence was observed. We also compare the Landsat classification with three other landcover schemes including the USGS's Global Landcover Classification, which is based on AVHRR data. Results from these landcover classifications are used as input for models that predict tropospheric ozone production and are used to investigate ozone control strategies.     

基于3S技术的土地利用/土地覆盖变化野外采样框架设计 ——以东北地区黑龙江省为例

土地利用/土地覆盖变化是全球环境变化的重要组成部分,随着3S技术的不断成熟和发展,运用RS、GPS和GIS技术进行土地利用/土地覆盖变化研究已成为一种越来越成熟的方式和手段。从空间抽样模型理论出发,以我国黑龙江省为例,运用RS、GPS和GIS技术,通过对黑龙江省道路网、土地利用区划、土地利用/土地覆盖类型、土地利用/土地覆盖1 km×1 km格网数据等空间信息分布的综合考虑、分析,设计了土地利用/土地覆盖变化的综合野外采样框架。框架主要包括采样区的布设、采样路线和采样点的选择等。由于以多层空间信息为采样依据,经实践检验,该采样框架具有经济实用等优点。     

Comparison of burnt area estimates derived from satellite products and national statistics in Europe

Burnt area maps based on satellite observations are frequently used in calculations related to fire regime, such as those of carbon dioxide emissions. Nevertheless, burnt area estimates between products vary widely, and validation against independent data is scarce, especially for Europe. Here we compare two active fire maps (the ATSR World Fire Atlas and the Moderate Resolution Imaging Spectroradiometer (MODIS) Active Fire Product) and two fire scars maps (the L3JRC and the MODIS Burned Area Product) to independent national statistics taken from 22 European countries between 1997 and 2008. We also tested the coincidence between satellite products derived by calculation of the fraction of active fires that were confirmed by a subsequent drop in reflectance. As a large proportion of fire pixels (between 40% and 66%, depending on the product) is located on urban land or crop fields, filtering out fires located on these land uses greatly improves the agreement between satellite-based burnt area estimates and national statistics and it also improves the coincidence between satellite products. The MODIS Active Fire Product appears to be most suitable for use as a proxy for burnt area patterns, showing a high correlation to national statistics (R² = 0.9), relatively low spatial and temporal heterogeneity and only a slight underestimation of the total burnt area (19 000 ha year^–1). Unfiltered products show cases of substantial wildfire overestimation in all products, mainly attributable to anthropogenic activity, in the case of active fire products, and drought-induced vegetation dieback, in that of fire scar maps. Thus, filtering out fires on anthropogenic land uses seems to be essential when analysing patterns of forest fires from satellite observations. However, if agricultural fires are to be included, a combination of MODIS Active Fire and MODIS Burned Area products is recommended. We obtained that such combination shows low temporal and spatial heterogeneity and the highest coincidence between satellite products (25%), although the correlation to national statistics is not very high (R² = 0.67) and clearly underestimates the total burnt area (187 000 ha year^–1).     

Land cover classification of tundra environments in the Arctic Lena Delta based on Landsat 7 ETM+ data and its application for upscaling of methane emissions

The Lena River Delta, situated in Northern Siberia (72.0-73.8° N, 122.0-129.5° E), is the largest Arctic delta and covers 29,000 km². Since natural deltas are characterised by complex geomorphological patterns and various types of ecosystems, high spatial resolution information on the distribution and extent of the delta environments is necessary for a spatial assessment and accurate quantification of biogeochemical processes as drivers for the emission of greenhouse gases from tundra soils. In this study, the first land cover classification for the entire Lena Delta based on Landsat 7 Enhanced Thematic Mapper (ETM+) images was conducted and used for the quantification of methane emissions from the delta ecosystems on the regional scale. Nine land cover classes of aquatic and terrestrial ecosystems in the wetland dominated (72%) Lena Delta could be defined by this classification approach. The mean daily methane emission of the entire Lena Delta was calculated with 10.35 mg CH₄ m^− 2 d^− 1. Taking our multi-scale approach into account we find that the methane source strength of certain tundra wetland types is lower than calculated previously on coarser scales.     

Integration of MONERIS and GREAT-ER in the decision support system for the German Elbe river basin

The Elbe-DSS is a computer based system for integrated river basin management of the German part of River Elbe basin. Simulation models are used to assess the efficiency of measures such as reforestation, changes of agricultural practices or the efficiency of wastewater treatment plants for achieving management targets. MONERIS and GREAT-ER are integrated into the Elbe-DSS to assess nutrient and pollutant loads. MONERIS calculates nutrient inputs from diffuse and point sources on a sub-catchment scale of about 1000 km². GREAT-ER is a tool for exposure assessment of point source emissions and considers fate in sewage treatment plants as well as degradation and transport in rivers. Both models make long-term predictions, but their spatial scales of operations differ. GREAT-ER divides the whole river network into small segments that are linked through a routing algorithm. The segments are coupled to MONERIS using accumulated flow length distribution. Linking the two models allows to distribute diffuse nutrient emissions calculated from MONERIS and point source emissions from GREAT-ER to the river network, where further elimination and transport processes are calculated. We exemplify the DSS in a study assessing the effects of different reforestation and erosion control measures on phosphate loads and concentrations in the river network.     

Time space stochastic modelling of agricultural landscapes for environmental issues

Since the initial point of Langran (1993) saying that Geographic Information Systems (GIS) were poorly equipped to handle temporal data, many researchers have sought to integrate the time dimension into GIS (Roddick et al., 2001). We present a time space modelling approach – and a generic software named ARPEnTAge – capable of clustering a territory based on its pluri-annual land-use organization. By adding the ability to represent, locate and visualize temporal changes in the territory, ARPEnTAge provides tools to build a Time-Dominant GIS. One main Markovian assumption is stated: the land-use succession in a given place depends only on the land-use successions in neighbouring plots. By means of stochastic models such as a Hierarchical Hidden Markov Model and a Markov random field, ARPEnTAge performs an unsupervised clustering of a territory in order to reveal patches characterized by time space regularities in the land-use successions. Two case studies are developed involving two territories carrying environmental issues. Those territories have various sizes and are parameterized using long term surveys and/or remote sensing data. In both cases, ARPEnTAge detects, locates and displays in a GIS the temporal changes. This gives valuable information on the spatial and time dynamics of the land-use organization of those territories.